# Multistage Pruning of CNN Based ECG Classifiers for Edge Devices

arXiv: https://arxiv.org/abs/2109.00516 · HTML: https://mlsanity.com/paper/2109.00516/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [Rethinking the Value of Network Pruning](https://mlsanity.com/paper/1810.05270/) (2018)
- [Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks](https://mlsanity.com/paper/1808.06866/) (2018)
- [Pruning from Scratch](https://mlsanity.com/paper/1909.12579/) (2019)
- [A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors](https://mlsanity.com/paper/2105.00528/) (2021)

## Built on by (most cited first, 1 of 1)

- [DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits](https://mlsanity.com/paper/2502.17446/) (2025)

## Abstract (arXiv)

Using smart wearable devices to monitor patients electrocardiogram (ECG) for real-time detection of arrhythmias can significantly improve healthcare outcomes. Convolutional neural network (CNN) based deep learning has been used successfully to detect anomalous beats in ECG. However, the computational complexity of existing CNN models prohibits them from being implemented in low-powered edge devices. Usually, such models are complex with lots of model parameters which results in large number of computations, memory, and power usage in edge devices. Network pruning techniques can reduce model complexity at the expense of performance in CNN models. This paper presents a novel multistage pruning technique that reduces CNN model complexity with negligible loss in performance compared to existing pruning techniques. An existing CNN model for ECG classification is used as a baseline reference. At 60% sparsity, the proposed technique achieves 97.7% accuracy and an F1 score of 93.59% for ECG classification tasks. This is an improvement of 3.3% and 9% for accuracy and F1 Score respectively, compared to traditional pruning with fine-tuning approach. Compared to the baseline model, we also achieve a 60.4% decrease in run-time complexity.